Evaluation of a Multispectral Camera on a UAV for Agricultural Applications

Australian farmers have used precision agriculture technology for many years with the use of ground – based and satellite systems. However, these systems require the use of vehicles in order to analyse a wide area which can be time consuming and cost ineffective. Also, satellite imagery may not be accurate for analysis. Low cost of Unmanned Aerial Vehicles (UAV) present an effective method of analysing large plots of agricultural fields. As the UAV can travel over long distances and fly over multiple plots, it allows for more data to be captured by a sampling device such as a multispectral camera and analysed thereafter. This would allow farmers to analyse the health of their crops and thus focus their efforts on certain areas which may need attention. This project evaluates a multispectral camera for use on a UAV for agricultural applications.

Programming an FPGA to Emulate the Dynamics of DC Machines

This paper presents the programming an FPGA (Field Programmable Gate Array) to emulate the dynamics of DC machines. FPGA allows high speed real time simulation with high precision. The described design includes block diagram representation of DC machine, which contain all arithmetic and logical operations. The real time simulation of the machine in FPGA is controlled by user interfaces they are Keypad interface, LCD display on-line and digital to analog converter. This approach provides emulation of electrical machine by changing the parameters. Separately Exited DC machine implemented and experimental results are presented.

The Use of Two Nematic Solvents in the Determination of Molecular Structure of Systems Providing Deceptively Simple Spectra

The use of two liquid crystals as solvents in the determination of molecular structure has been demonstrated for systems which do not provide structural information from studies in a single solvent owing to the fact that the spectra are deceptively simple, with the result that all the spectral parameters cannot be derived with reasonable precision. The specific system studied was 2-(p-bromophenyl)-4,6-dichloropyrimidine, for which relative inter-proton discances have been determined from the proton NMR spectra in two nematic solvents.

On Visual Detection of Highly-occluded Objects for Harvesting Automation in Horticulture

Developing accurate and reliable crop detection algorithms is an important step for harvesting automation in horticulture. This paper presents a novel approach to visual detection of highly-occluded fruits. We use a conditional random field (CRF) on multi-spectral image data (colour and Near-Infrared Reflectance, NIR) to model two classes: crop and background. To describe these two classes, we explore a range of visual-texture features including local binary pattern, histogram of oriented gradients, and learn auto-encoder features. The pro-posed methods are evaluated using hand-labelled images from a dataset captured on a commercial capsicum farm. Experimental results are presented, and performance is evaluated in terms of the Area Under the Curve (AUC) of the precision-recall curves.Our current results achieve a maximum performance of 0.81AUC when combining all of the texture features in conjunction with colour information.

Rapid assessment of genotype-by-environment interactions and heritability for growth rate in aquaculture species using in vitro fertilisation and DNA tagging

Commercial environments may receive only a fraction of expected genetic gains for growth rate as predicted from the selection environment This fraction is the result of undesirable genotype-by-environment interactions (G x E) and measured by the genetic correlation (r(g)) of growth between environments. Rapid estimates of genetic correlation achieved in one generation are notoriously difficult to estimate with precision. A new design is proposed where genetic correlations can be estimated by utilising artificial mating from cryopreserved semen and unfertilised eggs stripped from a single female. We compare a traditional phenotype analysis of growth to a threshold model where only the largest fish are genotyped for sire identification. The threshold model was robust to differences in family mortality differing up to 30%. The design is unique as it negates potential re-ranking of families caused by an interaction between common maternal environmental effects and growing environment. The design is suitable for rapid assessment of G x E over one generation with a true 0.70 genetic correlation yielding standard errors as low as 0.07. Different design scenarios were tested for bias and accuracy with a range of heritability values, number of half-sib families created, number of progeny within each full-sib family, number of fish genotyped, number of fish stocked, differing family survival rates and at various simulated genetic correlation levels

Load estimation with uncertainties from opportunistic sampling data: A semiparametric approach

We consider estimating the total load from frequent flow data but less frequent concentration data. There are numerous load estimation methods available, some of which are captured in various online tools. However, most estimators are subject to large biases statistically, and their associated uncertainties are often not reported. This makes interpretation difficult and the estimation of trends or determination of optimal sampling regimes impossible to assess. In this paper, we first propose two indices for measuring the extent of sampling bias, and then provide steps for obtaining reliable load estimates that minimizes the biases and makes use of informative predictive variables. The key step to this approach is in the development of an appropriate predictive model for concentration. This is achieved using a generalized rating-curve approach with additional predictors that capture unique features in the flow data, such as the concept of the first flush, the location of the event on the hydrograph (e.g. rise or fall) and the discounted flow. The latter may be thought of as a measure of constituent exhaustion occurring during flood events. Forming this additional information can significantly improve the predictability of concentration, and ultimately the precision with which the pollutant load is estimated. We also provide a measure of the standard error of the load estimate which incorporates model, spatial and/or temporal errors. This method also has the capacity to incorporate measurement error incurred through the sampling of flow. We illustrate this approach for two rivers delivering to the Great Barrier Reef, Queensland, Australia. One is a data set from the Burdekin River, and consists of the total suspended sediment (TSS) and nitrogen oxide (NO(x)) and gauged flow for 1997. The other dataset is from the Tully River, for the period of July 2000 to June 2008. For NO(x) Burdekin, the new estimates are very similar to the ratio estimates even when there is no relationship between the concentration and the flow. However, for the Tully dataset, by incorporating the additional predictive variables namely the discounted flow and flow phases (rising or recessing), we substantially improved the model fit, and thus the certainty with which the load is estimated.

An extended regression approach to estimating loads and their uncertainties in Great Barrier Reef catchments

There are numerous load estimation methods available, some of which are captured in various online tools. However, most estimators are subject to large biases statistically, and their associated uncertainties are often not reported. This makes interpretation difficult and the estimation of trends or determination of optimal sampling regimes impossible to assess. In this paper, we first propose two indices for measuring the extent of sampling bias, and then provide steps for obtaining reliable load estimates by minimizing the biases and making use of possible predictive variables. The load estimation procedure can be summarized by the following four steps: - (i) output the flow rates at regular time intervals (e.g. 10 minutes) using a time series model that captures all the peak flows; - (ii) output the predicted flow rates as in (i) at the concentration sampling times, if the corresponding flow rates are not collected; - (iii) establish a predictive model for the concentration data, which incorporates all possible predictor variables and output the predicted concentrations at the regular time intervals as in (i), and; - (iv) obtain the sum of all the products of the predicted flow and the predicted concentration over the regular time intervals to represent an estimate of the load. The key step to this approach is in the development of an appropriate predictive model for concentration. This is achieved using a generalized regression (rating-curve) approach with additional predictors that capture unique features in the flow data, namely the concept of the first flush, the location of the event on the hydrograph (e.g. rise or fall) and cumulative discounted flow. The latter may be thought of as a measure of constituent exhaustion occurring during flood events. The model also has the capacity to accommodate autocorrelation in model errors which are the result of intensive sampling during floods. Incorporating this additional information can significantly improve the predictability of concentration, and ultimately the precision with which the pollutant load is estimated. We also provide a measure of the standard error of the load estimate which incorporates model, spatial and/or temporal errors. This method also has the capacity to incorporate measurement error incurred through the sampling of flow. We illustrate this approach using the concentrations of total suspended sediment (TSS) and nitrogen oxide (NOx) and gauged flow data from the Burdekin River, a catchment delivering to the Great Barrier Reef. The sampling biases for NOx concentrations range from 2 to 10 times indicating severe biases. As we expect, the traditional average and extrapolation methods produce much higher estimates than those when bias in sampling is taken into account.

General ranked set sampling for efficient treatment comparisons

In treatment comparison experiments, the treatment responses are often correlated with some concomitant variables which can be measured before or at the beginning of the experiments. In this article, we propose schemes for the assignment of experimental units that may greatly improve the efficiency of the comparison in such situations. The proposed schemes are based on general ranked set sampling. The relative efficiency and cost-effectiveness of the proposed schemes are studied and compared. It is found that some proposed schemes are always more efficient than the traditional simple random assignment scheme when the total cost is the same. Numerical studies show promising results using the proposed schemes.

Merenkulun turvallisuuden koetinkiviä : Terminologisen tiedon poiminta teksteistä

The methodology of extracting information from texts has widely been described in the current literature. However, the methodology has been developed mainly for the purposes of other fields than terminology science. In addition, the research has been English language oriented. Therefore, there are no satisfactory language-independent methods for extracting terminological information from texts. The aim of the present study is to form the basis for a further improvement of methods for extraction of terminological information. A further aim is to determine differences in term extraction between subject groups with or without knowledge of the special field in question. The study is based on the theory of terminology, and has mainly a qualitative approach. The research material consists of electronically readable specialized texts in the subject domain of maritime safety. Textbooks, conference papers, research reports and articles from professional journals in Finnish and in Russian are included. The thesis first deals with certain term extraction methods. These are manual term identification and semi-automatic term extraction, the latter of which was carried out by using three commercial computer programs. The results of term extraction were compared and the recall and precision of the methods were evaluated. The latter part of the study is dedicated to the identification of concept relations. Certain linguistic expressions, which some researchers call knowledge probes, were applied to identify concept relations. The results of the present thesis suggest that special field knowledge is an advantage in manual term identification. However, in the candidate term lists the variation between subject groups was not as remarkable as it was between individual subjects. The term extraction software tested here produces candidate term lists which can be useful, but only after some manual work. Therefore, the work emphasizes the need to further develop term extraction software. Furthermore, the analyses indicate that there are a certain number of terms which were extracted by all the subjects and the software. These terms we call core terms. As the result of the experiment on linguistic expressions which signal concept relations, a proposal of Finnish and Russian knowledge probes in the field of maritime safety was made. The main finding was that it would be useful to combine the use of knowledge probes with semi-automatic term extraction since knowledge probes usually occur in the vicinity of terms.

Influence of environmental and instrumental variables on the non-invasive prediction of Brix in pineapple using near infrared spectroscopy

The Brix content of pineapple fruit can be non-invasively predicted from the second derivative of near infrared reflectance spectra. Correlations obtained using a NIRSystems 6500 spectrophotometer through multiple linear regression and modified partial least squares analyses using a post-dispersive configuration were comparable with that from a pre-dispersive configuration in terms of accuracy (e.g. coefficient of determination, R2, 0.73; standard error of cross validation, SECV, 1.01°Brix). The effective depth of sample assessed was slightly greater using the post-dispersive technique (about 20 mm for pineapple fruit), as expected in relation to the higher incident light intensity, relative to the pre-dispersive configuration. The effect of such environmental variables as temperature, humidity and external light, and instrumental variables such as the number of scans averaged to form a spectrum, were considered with respect to the accuracy and precision of the measurement of absorbance at 876 nm, as a key term in the calibration for Brix, and predicted Brix. The application of post-dispersive near infrared technology to in-line assessment of intact fruit in a packing shed environment is discussed.

Kulttuurisia kohtaamisia : Suomalaiset kuvataiteilijat ja Rooma 1800-luvulla

Dozens of Finnish artists, practically all the professional sculptors and painters, travelled to and stayed in Rome during the 19th century. The study at hand concentrates for the first time on the Finnish artists in Rome in corpore, and analyses their way of life based on a broad variety of previously unknown and unexplored sources from a number of archives in both Scandinavia and Rome. The extensive corpus of source material is scrutinized with microhistorical precision from the point of view of cultural history. The new information thus achieved adds to the previous knowledge of Rome s often overlooked importance as a source of inspiration in Scandinavian culture in general and significantly clarifies our understanding of the development of Finnish artistic life and cultural identity in the 19th century. The study proves that in Finland, like in all of Europe, the stay in Rome was considered to be a necessary part of becoming a true artist. Already the journey was an integral part of the encounter with Rome, corresponding with the civilized ideal of the period. The stay in Rome provided a northern artist with overwhelming opportunities that were incomparable to the unestablished and modest forms of artistic life Finland could offer. Without domestic artistic institutions or traditions, the professional status of Finnish painters and sculptors took shape abroad, firstly through the encounter with Rome and the different networks the Finnish artists belonged to during and after their stay in the eternal city. The Finnish artists were an integral part of the international artistic community in the cultural capital of Europe, which gave a totally new impetus to their work and contributed to their cosmopolitan identification. For these early masters of Finnish art, the Scandinavian communality and universal artistic identity seemed to be more significant than their nationality. In all, the scrutiny of Finnish artists in their wide social, ideological and international framework gives an interesting aspect to the cultural ambiance of the 19th century, in both Rome and Finland. The study highlights many long-forgotten artists who were influential in shaping Finnish art, culture and identity in their time.

Overfitting Bayesian mixture models with an unknown number of components

This paper proposes solutions to three issues pertaining to the estimation of finite mixture models with an unknown number of components: the non-identifiability induced by overfitting the number of components, the mixing limitations of standard Markov Chain Monte Carlo (MCMC) sampling techniques, and the related label switching problem. An overfitting approach is used to estimate the number of components in a finite mixture model via a Zmix algorithm. Zmix provides a bridge between multidimensional samplers and test based estimation methods, whereby priors are chosen to encourage extra groups to have weights approaching zero. MCMC sampling is made possible by the implementation of prior parallel tempering, an extension of parallel tempering. Zmix can accurately estimate the number of components, posterior parameter estimates and allocation probabilities given a sufficiently large sample size. The results will reflect uncertainty in the final model and will report the range of possible candidate models and their respective estimated probabilities from a single run. Label switching is resolved with a computationally light-weight method, Zswitch, developed for overfitted mixtures by exploiting the intuitiveness of allocation-based relabelling algorithms and the precision of label-invariant loss functions. Four simulation studies are included to illustrate Zmix and Zswitch, as well as three case studies from the literature. All methods are available as part of the R package Zmix, which can currently be applied to univariate Gaussian mixture models.

A comparison of the excretion rate of endogenous purine derivatives in the urine of Bos indicus and Bos taurus steers

Estimates of microbial crude protein (MCP) production by ruminants, using a method based on the excretion of purine derivatives in urine, require an estimate of the excretion of endogenous purine derivatives (PD) by the animal. Current methods allocate a single value to all cattle. An experiment was carried out to compare the endogenous PD excretion in Bos taurus and high-content B. indicus (hereafter, B. indicus) cattle. Five Holstein–Friesian (B. taurus) and 5 Brahman (> 75% B. indicus) steers (mean liveweight 326 ± 3.0 kg) were used in a fasting study. Steers were fed a low-quality buffel grass (Cenchrus ciliaris; 59.4 g crude protein/kg dry matter) hay at estimated maintenance requirements for 19 days, after which hay intake was incrementally reduced for 2 days and the steers were fasted for 7 days. The excretion of PD in urine was measured daily for the last 6 days of the fasting period and the mean represented the daily endogenous PD excretion. Excretion of endogenous PD in the urine of B. indicus steers was less than half that of the B. taurus steers (190 µmol/kg W0.75.day v. 414 µmol/kg W0.75.day; combined s.e. 37.2 µmol/kg W0.75.day; P < 0.001). It was concluded that the use of a single value for endogenous PD excretion is inappropriate for use in MCP estimations and that subspecies-specific values would improve precision.

Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environments

Variable-rate technologies and site-specific crop nutrient management require real-time spatial information about the potential for response to in-season crop management interventions. Thermal and spectral properties of canopies can provide relevant information for non-destructive measurement of crop water and nitrogen stresses. In previous studies, foliage temperature was successfully estimated from canopy-scale (mixed foliage and soil) temperatures and the multispectral Canopy Chlorophyll Content Index (CCCI) was effective in measuring canopy-scale N status in rainfed wheat (Triticum aestivum L.) systems in Horsham, Victoria, Australia. In the present study, results showed that under irrigated wheat systems in Maricopa, Arizona, USA, the theoretical derivation of foliage temperature unmixing produced relationships similar to those in Horsham. Derivation of the CCCI led to an r2 relationship with chlorophyll a of 0.53 after Zadoks stage 43. This was later than the relationship (r2 = 0.68) developed for Horsham after Zadoks stage 33 but early enough to be used for potential mid-season N fertilizer recommendations. Additionally, ground-based hyperspectral data estimated plant N (g kg)1) in Horsham with an r2 = 0.86 but was confounded by water supply and N interactions. By combining canopy thermal and spectral properties, varying water and N status can potentially be identified eventually permitting targeted N applications to those parts of a field where N can be used most efficiently by the crop.

Near infrared reflectance as a rapid and inexpensive surrogate measure for fatty acid composition and oil content in peanuts (Arachis hypogaea L.)

The fatty acid composition of ground nuts (Arachis hypogaea L.) commonly known as peanuts, is an important consideration when a new variety is being released. The composition impacts on nutrition and, importantly, self-life of peanut products. To select for suitable breeding material, it was necessary to develop a rapid, non-derstructive and cost-efficient method. Near infrared spectroscopy was chosen as that methodology. Calibrations were developed for two major fatty-acid components, oleic and linoleic acids and two minor components, palmitic and stearic acids, as well as total oil content. Partial least squares models indicated a high level of precision with a squared multiple correlation coefficient of greater than 0.90 for each constitutent. Standard errors for prediction for oleic, linoleic, palmitic, stearic acids and total oil content were 6.4%, 4.5%, 0.8%, 0.9% and 1.3% respectively. The results demonstrated that reasonable calibrations could be developed to predict oil composition and content of peanuts for a breeding programme.